"""simple docstring"""



import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


SCREAMING_SNAKE_CASE__:List[Any]									=			"""true"""



def 	_lowerCamelCase(	a   ,    a=8_2   ,    a=1_6				):
       set_seed(4_2				)
       __a       =    RegressionModel()
       __a       =    deepcopy(a				)
       __a       =    RegressionDataset(length=a				)
       __a       =    DataLoader(a   ,    batch_size=a				)
       model.to(accelerator.device				)
       __a ,   __a       =    accelerator.prepare(a   ,    a				)
       return model, ddp_model, dataloader



def 	_lowerCamelCase(	a   ,    a=False				):
       __a       =    AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"				)
       __a       =    load_dataset("glue"   ,    "mrpc"   ,    split="validation"				)

       def tokenize_function(a				):
              __a       =    tokenizer(examples["sentence1"]   ,    examples["sentence2"]   ,    truncation=a   ,    max_length=a				)
              return outputs

       with accelerator.main_process_first():
              __a       =    dataset.map(
                  a   ,    batched=a   ,    remove_columns=["idx", "sentence1", "sentence2"]   ,    )

       __a       =    tokenized_datasets.rename_column("label"   ,    "labels"				)

       def collate_fn(a				):
              if use_longest:
                     return tokenizer.pad(a   ,    padding="longest"   ,    return_tensors="pt"				)
              return tokenizer.pad(a   ,    padding="max_length"   ,    max_length=1_2_8   ,    return_tensors="pt"				)

       return DataLoader(a   ,    shuffle=a   ,    collate_fn=a   ,    batch_size=1_6				)



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    Accelerator(dispatch_batches=a   ,    split_batches=a				)
       __a       =    get_dataloader(a   ,    not dispatch_batches				)
       __a       =    AutoModelForSequenceClassification.from_pretrained(
           "hf-internal-testing/mrpc-bert-base-cased"   ,    return_dict=a				)
       __a ,   __a       =    accelerator.prepare(a   ,    a				)
       return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator



def 	_lowerCamelCase(	a   ,    a   ,    a				):
       __a       =    []
       for batch in dataloader:
              __a ,   __a       =    batch.values()
              with torch.no_grad():
                     __a       =    model(a				)
                     __a ,   __a       =    accelerator.gather_for_metrics((logit, target)				)
                     logits_and_targets.append((logit, target)				)
       __a ,   __a       =    [], []
       for logit, targ in logits_and_targets:
              logits.append(a				)
              targs.append(a				)
       __a ,   __a       =    torch.cat(a				), torch.cat(a				)
       return logits, targs



def 	_lowerCamelCase(	a   ,    a=8_2   ,    a=False   ,    a=False   ,    a=1_6				):
       __a ,   __a ,   __a       =    get_basic_setup(a   ,    a   ,    a				)
       __a ,   __a       =    generate_predictions(a   ,    a   ,    a				)
       assert (
           len(a				) == num_samples
       ), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(a				)}"



def 	_lowerCamelCase(	a = False   ,    a = False				):
       __a       =    evaluate.load("glue"   ,    "mrpc"				)
       __a ,   __a       =    get_mrpc_setup(a   ,    a				)
       # First do baseline
       __a ,   __a ,   __a       =    setup["no"]
       model.to(a				)
       model.eval()
       for batch in dataloader:
              batch.to(a				)
              with torch.inference_mode():
                     __a       =    model(**a				)
              __a       =    outputs.logits.argmax(dim=-1				)
              metric.add_batch(predictions=a   ,    references=batch["labels"]				)
       __a       =    metric.compute()

       # Then do distributed
       __a ,   __a ,   __a       =    setup["ddp"]
       model.eval()
       for batch in dataloader:
              with torch.inference_mode():
                     __a       =    model(**a				)
              __a       =    outputs.logits.argmax(dim=-1				)
              __a       =    batch["labels"]
              __a ,   __a       =    accelerator.gather_for_metrics((preds, references)				)
              metric.add_batch(predictions=a   ,    references=a				)
       __a       =    metric.compute()

       for key in "accuracy f1".split():
              assert math.isclose(
                  baseline[key]   ,    distributed[key]				), F"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"



def 	_lowerCamelCase(	):
       __a       =    Accelerator(split_batches=a   ,    dispatch_batches=a				)
       if accelerator.is_local_main_process:
              datasets.utils.logging.set_verbosity_warning()
              transformers.utils.logging.set_verbosity_warning()
       else:
              datasets.utils.logging.set_verbosity_error()
              transformers.utils.logging.set_verbosity_error()
       # These are a bit slower so they should only be ran on the GPU or TPU
       if torch.cuda.is_available() or is_tpu_available():
              if accelerator.is_local_main_process:
                     print("**Testing gather_for_metrics**"				)
              for split_batches in [True, False]:
                     for dispatch_batches in [True, False]:
                            if accelerator.is_local_main_process:
                                   print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"				)
                            test_mrpc(a   ,    a				)
                            accelerator.state._reset_state()
       if accelerator.is_local_main_process:
              print("**Test torch metrics**"				)
       for split_batches in [True, False]:
              for dispatch_batches in [True, False]:
                     __a       =    Accelerator(split_batches=a   ,    dispatch_batches=a				)
                     if accelerator.is_local_main_process:
                            print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"				)
                     test_torch_metrics(a   ,    9_9				)
                     accelerator.state._reset_state()
       if accelerator.is_local_main_process:
              print("**Test last batch is not dropped when perfectly divisible**"				)
       __a       =    Accelerator()
       test_torch_metrics(a   ,    5_1_2				)
       accelerator.state._reset_state()



def 	_lowerCamelCase(	a				):
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
   main()

"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
   import PIL

SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)



def 	_lowerCamelCase(	a				):
       if isinstance(a   ,    (list, tuple)				) and isinstance(videos[0]   ,    (list, tuple)				) and is_valid_image(videos[0][0]				):
              return videos

       elif isinstance(a   ,    (list, tuple)				) and is_valid_image(videos[0]				):
              return [videos]

       elif is_valid_image(a				):
              return [[videos]]

       raise ValueError(F"Could not make batched video from {videos}"				)






class  snake_case__      (       snake_case_       ):
 _snake_case       :					List[Any]							  =						["""pixel_values"""]


 def __init__( self    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = PILImageResampling.BILINEAR    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = True    ,		lowerCamelCase = 1 / 255    ,		lowerCamelCase = True    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase						)
        __a       =    size if size is not None else {"shortest_edge": 256}
        __a       =    get_size_dict(lowerCamelCase    ,		default_to_square=lowerCamelCase						)
        __a       =    crop_size if crop_size is not None else {"height": 224, "width": 224}
        __a       =    get_size_dict(lowerCamelCase    ,		param_name="crop_size"						)

        __a       =    do_resize
        __a       =    size
        __a       =    do_center_crop
        __a       =    crop_size
        __a       =    resample
        __a       =    do_rescale
        __a       =    rescale_factor
        __a       =    offset
        __a       =    do_normalize
        __a       =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __a       =    image_std if image_std is not None else IMAGENET_STANDARD_STD


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = PILImageResampling.BILINEAR    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    get_size_dict(lowerCamelCase    ,		default_to_square=lowerCamelCase						)
        if "shortest_edge" in size:
               __a       =    get_resize_output_image_size(lowerCamelCase    ,		size["shortest_edge"]    ,		default_to_square=lowerCamelCase						)
        elif "height" in size and "width" in size:
               __a       =    (size["height"], size["width"])
        else:
               raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"						)
        return resize(lowerCamelCase    ,		size=lowerCamelCase    ,		resample=lowerCamelCase    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    get_size_dict(lowerCamelCase						)
        if "height" not in size or "width" not in size:
               raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"						)
        return center_crop(lowerCamelCase    ,		size=(size["height"], size["width"])    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    image.astype(np.floataa						)
        if offset:
               __a       =    image - (scale / 2)
        return rescale(lowerCamelCase    ,		scale=lowerCamelCase    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        return normalize(lowerCamelCase    ,		mean=lowerCamelCase    ,		std=lowerCamelCase    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = ChannelDimension.FIRST    ,		):
        if do_resize and size is None or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."						)

        if do_center_crop and crop_size is None:
               raise ValueError("Crop size must be specified if do_center_crop is True."						)

        if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."						)

        if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("Image mean and std must be specified if do_normalize is True."						)

        if offset and not do_rescale:
               raise ValueError("For offset, do_rescale must also be set to True."						)

        # All transformations expect numpy arrays.
        __a       =    to_numpy_array(lowerCamelCase						)

        if do_resize:
               __a       =    self.resize(image=lowerCamelCase    ,		size=lowerCamelCase    ,		resample=lowerCamelCase						)

        if do_center_crop:
               __a       =    self.center_crop(lowerCamelCase    ,		size=lowerCamelCase						)

        if do_rescale:
               __a       =    self.rescale(image=lowerCamelCase    ,		scale=lowerCamelCase    ,		offset=lowerCamelCase						)

        if do_normalize:
               __a       =    self.normalize(image=lowerCamelCase    ,		mean=lowerCamelCase    ,		std=lowerCamelCase						)

        __a       =    to_channel_dimension_format(lowerCamelCase    ,		lowerCamelCase						)
        return image






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = ChannelDimension.FIRST    ,		**lowerCamelCase    ,		):
        __a       =    do_resize if do_resize is not None else self.do_resize
        __a       =    resample if resample is not None else self.resample
        __a       =    do_center_crop if do_center_crop is not None else self.do_center_crop
        __a       =    do_rescale if do_rescale is not None else self.do_rescale
        __a       =    rescale_factor if rescale_factor is not None else self.rescale_factor
        __a       =    offset if offset is not None else self.offset
        __a       =    do_normalize if do_normalize is not None else self.do_normalize
        __a       =    image_mean if image_mean is not None else self.image_mean
        __a       =    image_std if image_std is not None else self.image_std

        __a       =    size if size is not None else self.size
        __a       =    get_size_dict(lowerCamelCase    ,		default_to_square=lowerCamelCase						)
        __a       =    crop_size if crop_size is not None else self.crop_size
        __a       =    get_size_dict(lowerCamelCase    ,		param_name="crop_size"						)

        if not valid_images(lowerCamelCase						):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."						)

        __a       =    make_batched(lowerCamelCase						)

        __a       =    [
            [
                self._preprocess_image(
                    image=lowerCamelCase    ,		do_resize=lowerCamelCase    ,		size=lowerCamelCase    ,		resample=lowerCamelCase    ,		do_center_crop=lowerCamelCase    ,		crop_size=lowerCamelCase    ,		do_rescale=lowerCamelCase    ,		rescale_factor=lowerCamelCase    ,		offset=lowerCamelCase    ,		do_normalize=lowerCamelCase    ,		image_mean=lowerCamelCase    ,		image_std=lowerCamelCase    ,		data_format=lowerCamelCase    ,		)
                for img in video
            ]
            for video in videos
        ]

        __a       =    {"pixel_values": videos}
        return BatchFeature(data=lowerCamelCase    ,		tensor_type=lowerCamelCase						)
